ML13255A376: Difference between revisions

From kanterella
Jump to navigation Jump to search
(Created page by program invented by StriderTol)
(Created page by program invented by StriderTol)
 
(2 intermediate revisions by the same user not shown)
Line 16: Line 16:


=Text=
=Text=
{{#Wiki_filter:SOARCA Peach Bottom Uncertainty Analysis (UA)ACRSBriefing ACRS BriefingTinaGhosh,PhD Tina Ghosh, PhDRES/DSA/AABSeptember 16, 2013 AgendaACRStMACCS2th
{{#Wiki_filter:SOARCA Peach Bottom Uncertainty Analysis (UA)
*ACRS comments on MACCS2 weather uncertainty integration and convergence of resultsandstaffresponses results, and staff responses
ACRS Briefing Tina Ghosh, PhD RES/DSA/AAB September 16, 2013
*MELCORparametersofinterest
*MELCOR parameters of interestMACCS2parametersofinterest
*MACCS2 parameters of interest2 MELCOR -MACCS2 -WeatherUncertainty Weather Uncertainty Integration ACRS Comment: *For the combined MELCOR-MACCS2 results, the report currentlypresentsonlyresultsaveragedovertheweather currently presents only results averaged over the weather trials.  *The report should also present results that include and dilthfllthlttit display the full weather aleatory uncer tainty3 Conditional mean, individual latent cancer fatalit y (LCF) risk (per y()(pevent) for combined results (865) with LNT model 0100200300400500-10 miles0-20 miles0-30 miles0-40 miles0-50 miles5th3110549105341052210519105percentile 3.1x10-54.9x10-53.4x10-52.2x10-51.9x10-5Median1.3x10-41.9x10-41.3x10-48.7x10-57.1x10-5Mean1.7x10-42.8x10-42.0x10-41.3x10-41.0x10-495thpercentile 4.2x10-47.7x10-45.3x10-43.4x10-42.7x10-4SOARCAUA4SOARCAUABaseCase9.0x10-58.3x10-55.8x10-53.7x10-53.0x10-5 Conditional Individual LCF Risk (per Event) CCDFs for Combined Aleatory dEitiUtitd and Epistemic Uncertainty and Epistemic Uncertainty with Aleatory


Means10.80.910-10 miles Aleatory Mean0-20 miles Aleatory Mean0-50 miles Aleatory Mean0-10 miles Epistemic & Aleatory 020ilEiti&Alt 050.6 0.70-20 miles Epistemic & Aleatory0-50 miles Epistemic & Aleatory DF0.3 0.40.5CCD00.1 0.25Individual LCF Risk 01.0E-061.0E-051.0E-041.0E-031.0E-02 MACCS2 and Weather UncertaintiesforPrompt Uncertainties for Prompt Fatality RiskACRSComment:
Agenda
ACRS Comment: *Select the MELCOR realization that produced the largest conditional prompt fatality consequences in the current SOARCA uncertainty results. *For that realization, sample from the 350 MACCS2 input parametersandforeachepistemicsamplegenerate984 parameters
* ACRS comments  t on MACCS2 weatherth uncertainty integration and convergence of results and staff responses results,
, and for each epistemic sample generate 984 weather cases to derive an uncertainty distribution for the conditional prompt fatality consequences at each ditdistance. *Demonstrate convergence of the combined MACCS2-weatheruncertaintyanalysisresults.
* MELCOR parameters of interest
6weather uncertainty analysis results.
* MACCS2 parameters of interest 2
MACCS2 and Weather UncertaintiesforPrompt Uncertainties for Prompt Fatality Risk (cont.)
 
MELCOR - MACCS2 -
Weather Uncertainty Integration ACRS Comment:
* For the combined MELCOR-MACCS2 results, the report currently presents only results averaged over the weather trials.
* The report should also present results that include and di l the display th full f ll weather th aleatory l t    uncertainty t i t 3
 
Conditional mean, individual latent cancer fatality y ((LCF)) risk (p (per event) for combined results (865) with LNT model 0 10 0-10      0 20 0-20      0 30 0-30      0 40 0-40      0 50 0-50 miles    miles    miles      miles      miles 5th 3.1x10 3 1 10-55 4 4.9x10 9 10-55 3 3.4x10 4 10-55 2 2.2x10 2 10-55 1 1.9x10 9 10-55 percentile Median    1.3x10-4 1.9x10-4 1.3x10-4 8.7x10-5 7.1x10-5 Mean      1.7x10-4 2.8x10-4 2.0x10-4 1.3x10-4 1.0x10-4 95th 4.2x10-4 7.7x10-4 5.3x10-4 3.4x10-4 2.7x10-4 percentile SOARCA UA 9.0x10-5 8.3x10-5 5.8x10-5 3.7x10-5 3.0x10-5 Base Case 4
 
Conditional Individual LCF Risk (per Event) CCDFs for Combined Aleatory and d Epistemic E i t i Uncertainty U    t i t andd Epistemic Uncertainty with Aleatory Means 1                                        0-10 miles Aleatory Mean 0.9                                        0-20 miles Aleatory Mean 0-50 miles Aleatory Mean 0.8                                        0-10 miles Epistemic & Aleatory 0 20 miles 0-20 il E Epistemic i t i & Al Aleatory t
0.7                                        0-50 miles Epistemic & Aleatory 0.6 05 0.5 CCD DF 0.4 0.3 0.2 0.1 0
1.0E-06  1.0E-05          1.0E-04          1.0E-03              1.0E-02 Individual LCF Risk 5
 
MACCS2 and Weather Uncertainties for Prompt Fatality Risk ACRS Comment:
* Select the MELCOR realization that produced the largest conditional prompt fatality consequences in the current SOARCA uncertainty results.
* For that realization, sample from the 350 MACCS2 input parameters and for each epistemic sample generate 984 parameters, weather cases to derive an uncertainty distribution for the conditional prompt fatality consequences at each di t distance.
* Demonstrate convergence of the combined MACCS2-weather uncertainty analysis results.
6
 
MACCS2 and Weather Uncertainties for Prompt Fatality Risk (cont.)
Approach:
Approach:
* MELCOR Replicate 2, Realization 291 identified as the source term that produced the largest conditional prompt fatality risk consequence
* For that source term, three Monte Carlo runs of sample size 1000 were completed (Runs 3 3, 4 4, 5) using three different LHS random seeds for the 350 MACCS2 input parameters
* The same 984 weather trials were used 7
Conditional, mean, individual prompt-fatality p    p        y risk (p (per event))
statistics for the MACCS2 Uncertainty Analysis for specified circular areas (Run 1) 0-1.3    0-2.5    0-3.5      0-7    0-10 miles    miles    miles    miles    miles Mean    4.5x10-7 8.9x10-8 3.5x10-8 8.3x10-9 4.8x10-9 Median    00 0.0      00 0.0      00 0.0      00 0.0      00 0.0 75th percent p          0.0      0.0      0.0      0.0      0.0
-ile 95th 1.9x10 percent 1 9x10-6 3 3.5x10 5x10-8    00 0.0      00 0.0      00 0.0
-ile 8
Run 3-5 conditional, mean, individual p prompt-fatality p        y risk (p (per event) statistics for specified circular areas 0-1.3    0-2.5    0-3.5                0-10 0-7 miles miles    miles    miles                miles Run 3 3.3E-06  1.0E-06  3.4E-07    4.7E-08  9.5E-09 M
Mean  R Run 4 3 3E 06 3.3E-06  9 4E 07 9.4E-07  3 0E 07 3.0E-07    4 2E 08 4.2E-08  8 9E 09 8.9E-09 Run 5 3.2E-06  9.8E-07  3.0E-07    4.7E-08  1.3E-08 Run 3 4.9E-07  1.2E-07    0.0        0.0        0.0 Median Run 4  3 3E 06 3.3E-06  9 4E 07 9.4E-07    00 0.0        00 0.0        00 0.0 Run 5 3.2E-06  9.8E-07    0.0        0.0        0.0 75th  Run 3 4.0E-06  1.0E-06  2.0E-07    3.8E-09      0.0 percent Run 4 3 7E 06 3.7E-06  8 8E 07 8.8E-07  2 2E 07 2.2E-07    1 1E 08 1.1E-08      00 0.0
  -ile  Run 5 3.9E-06  9.6E-07  1.9E-07    8.2E-09      0.0 95th  Run 3 1.4E-05  4.1E-06  1.5E-06    2.1E-07  1.2E-08 percent Run 4 1 6E 05 1.6E-05  4 7E 06 4.7E-06  1 8E 06 1.8E-06    2 3E 07 2.3E-07      00 0.0
  -ile  Run 5 1.4E-05  4.4E-06  1.6E-06    2.0E-07      0.0 9
Runs 3-5 and Run 1 Conditional, Mean Individual Prompt Fatality Risk (per Event) Epistemic Uncertainty CCDF, at 1.3 Miles 1
0.1 0-1.3 miles Run 1 CCD DF 0-1.3 miles Run 3 0.01 0-1.3 miles Run 4 0-1.3 miles Run 5 0.001 1.0E-11 1.0E-10 1.0E-09 1.0E-08 1.0E-07 1.0E-06 1.0E-05 1.0E-04 Individual Prompt Fatality Risk per Event 10
Runs 3-5 and Run 1 Conditional, Mean Individual Prompt Fatality Risk (per Event) Epistemic Uncertainty CCDF, at 3.5 Miles 1
0.1 0-3.5 miles Run 1 0-3 0 3.5 5 miles Run 3 CCD DF 0-3.5 miles Run 4 0.01 0-3.5 miles Run 5 0.001 0 001 1.0E-12 1.0E-11 1.0E-10 1.0E-09 1.0E-08 1.0E-07 1.0E-06 1.0E-05 Individual Prompt Fatality Risk per Event 11
MACCS2 and Weather Uncertainties for LCF Risk 1 ACRS Comment:
* Select the MELCOR realization that produced the largest conditional LCF fatality consequences in the current SOARCA uncertainty results.
* For that realization, sample from the 350 MACCS2 input parameters and for each epistemic sample generate 984 parameters, weather cases to derive an uncertainty distribution for the conditional LCF fatality consequences at each distance.
* Demonstrate convergence of the combined MACCS2-weather uncertainty analysis results.
12
MACCS2 and Weather Uncertainties for LCF Risk 1 (cont.)
Approach:
Approach:
*MELCOR Replicate 2, Realization 291 identified as the source term that produced the largest conditional prompt fatality risk consequence *For that source term, three Monte Carlo runs of sample size1000werecompleted(Runs345)usingthree size 1000 were completed (Runs 3, 4, 5) using three different LHS random seeds for the 350 MACCS2 input  
* MELCOR Replicate 3, Realization 46 identified as the source term that produced the largest conditional LCF risk consequence
* For that source term, three Monte Carlo runs of sample size 1000 were completed (Runs 6 6, 7 7, 8) using three different LHS random seeds for the 350 MACCS2 input parameters
* The same 984 weather trials were used 13


parameters*The same 984 weather trials were used 7
Run 6-8 Combined Aleatory and Epistemic Uncertainty Conditional Individual LCF Risk (per Event) CCDF 1
Conditional, mean, individual prompt-fatalit y risk (per event) ppy(p)statistics for the MACCS2 Uncertainty Analysis for specified circularareas(Run1) circular areas (Run 1)0-1.3 miles0-2.5 miles0-3.5 miles0-7 miles0-10 milesmilesmilesMean4.5x10-78.9x10-83.5x10-88.3x10-94.8x10-9Median0000000000Median0.00.00.00.00.075thpercent0.00.00.00.00.0 p-ile95thpercent19x10-635x10-80000008percent-ile1.9x103.5x100.00.00.0 Run 3-5 conditional, mean, individual prompt-fatalit y risk (per ppy(pevent) statistics for specified
0.9                                              0-10 miles Run 6 0.8                                              0-10 miles Run 7 07 0.7                                             0-10 miles Run 8 0.6                                              0-50 miles Run 6 0.5                                              0-50 miles Run 7 CC CDF 0.4                                              0-50 miles Run 8 0.3 02 0.2 0.1 0
1.E-05              1.E-04                1.E-03              1.E-02 Individual Latent Cancer Fatality Risk per Event 14


circular areas 0-1.3 miles0-2.5 miles0-3.5 miles0-7 miles 0-10 milesRun 33.3E-061.0E-063.4E-074.7E-089.5E-09 MR433E0694E0730E0742E0889E09MeanRun 43.3E-069.4E-073.0E-074.2E-088.9E-09Run 53.2E-069.8E-073.0E-074.7E-081.3E-08 Run 34.9E-071.2E-070.00.00.0 MedianRun433E0694E07000000MedianRun 43.3E-069.4E-070.00.00.0Run 53.2E-069.8E-070.00.00.0 75thRun 34.0E-061.0E-062.0E-073.8E-090.0 percentRun437E0688E0722E0711E0800percentRun 43.7E-068.8E-072.2E-071.1E-080.0-ileRun 53.9E-069.6E-071.9E-078.2E-090.0 95thRun 31.4E-054.1E-061.5E-062.1E-071.2E-08 percentRun416E0547E0618E0623E07009percentRun 41.6E-054.7E-061.8E-062.3E-070.0-ileRun 51.4E-054.4E-061.6E-062.0E-070.0 Runs 3-5 and Run1 Conditional, Mean Individual Prompt Fatality Risk (per Event) Epistemic Uncertainty
Runs 6-8 and Run 1 Epistemic Uncertainty with Aleatory Mean, Conditional Individual LCF Risk (per Event) CCDFs 1
0-10 0 10 miles Run 1 0.9 0-10 miles Run 6 0.8                                                0-10 miles Run 7 07 0.7                                                 0-10 0 10 miles Run 8 0-50 miles Run 1 0.6 0-50 miles Run 6 0.5                                                0-50 miles Run 7 CCD DF  0.4                                                0-50 miles Run 8 0.3 0.2 0.1 0
1.0E-05                1.0E-04                  1.0E-03 Individual Latent Cancer Fatality Risk per Event 15


CCDF, at 1.3 Miles 10.10-1.3 miles Run 1 DF0.010-1.3 miles Run 3 0-1.3 miles Run 4 CCD0.0010-1.3 miles Run 5 10Individual Prompt Fatality Risk per Event1.0E-111.0E-101.0E-091.0E-081.0E-071.0E-061.0E-051.0E-04 Runs 3-5 and Run1 Conditional, Mean Individual Prompt Fatality Risk (per Event) Epistemic Uncertainty
MACCS2 and Weather Uncertainties for LCF Risk 2 ACRS Comment:
* Select a MELCOR realization that produced a small, but non-zero, contribution to the conditional LCF fatality consequences in the current SOARCA uncertainty results.
realization sample from the 350 MACCS2 input
* For that realization, parameters, and for each epistemic sample generate 984 weather cases to derive an uncertainty distribution for the conditional diti    l LCF ffatality t lit consequences att each h di distance.
t
* Demonstrate convergence of the combined MACCS2-weather uncertainty analysis results.
16


CCDF, at 3.5 Miles 10.10-3.5 miles Run 1 0-35milesRun3 DF0.0103.5 miles Run 30-3.5 miles Run 4 0-3.5 miles Run 5 CCD000111Individual Prompt Fatality Risk per Event 0.0011.0E-121.0E-111.0E-101.0E-091.0E-081.0E-071.0E-061.0E-05 MACCS2 and Weather Uncertainties for LCF Risk 1ACRSComment:
MACCS2 and Weather Uncertainties for LCF Risk 2 (cont.)
ACRS Comment: *Select the MELCOR realization that produced the largest conditional LCF fatality consequences in the current SOARCA uncertainty results.  *For that realization, sample from the 350 MACCS2 input parametersandforeachepistemicsamplegenerate984 parameters
, and for each epistemic sample generate 984 weather cases to derive an uncertainty distribution for the conditional LCF fatality consequences at each distance.  *Demonstrate convergence of the combined MACCS2-weather uncertainty analysis results.
12 MACCS2 and Weather UncertaintiesforLCFRisk1 Uncertainties for LCF Risk 1 (cont.)Approach:
Approach:
Approach:
*MELCOR Replicate 3, Realization 46 identified as the source term that produced the largest conditional LCF risk consequence  *For that source term, three Monte Carlo runs of sample size1000werecompleted(Runs678)usingthree size 1000 were completed (Runs 6, 7, 8) using three different LHS random seeds for the 350 MACCS2 input
* Three representative p              source terms were chosen
* First an initial MACCS2 run (Run 2) used all 865 source terms while all MACCS2 parameters were set to their SOARCA point estimate values values.
  - To assess the influence of the source term when MACCS2 parameters are fixed 17


parameters*The same 984 weather trials were used 13 Run 6-8 Combined Aleatory and Epistemic Uncertainty Conditional Individual LCF Risk (per Event) CCDF 1070.80.90-10 miles Run 6 0-10 miles Run 7 0-10milesRun8 0.50.60.7CDF0-10 miles Run 80-50 miles Run 6 0-50 miles Run 7 020.30.4CC0-50 miles Run 8 00.10.214Individual Latent Cancer Fatality Risk per Event1.E-051.E-041.E-031.E-02 Runs 6-8 and Run 1 Epistemic Uncertainty with Aleatory Mean, Conditional Individual LCF Risk (per  
Run 2 Conditional Mean, Individual LCF Risk (per Event) for 865 Source Terms and Fixed CCDF 1
0.9 0-10 miles 0.8                                    0-20 miles 0.7                                   0-30 miles 0.6                                    0-40 miles 0.5                                    0-50 miles 04 0.4 CCD DF 0.3 0.2 0.1 0
1 0E 05 1.0E-05      1 0E 04 1.0E-04            1 0E 03 1.0E-03          1 0E 02 1.0E-02 Individual LCF Risk per Event 18


Event) CCDFs 1010milesRun1 070.80.90-10 miles Run 10-10 miles Run 6 0-10 miles Run 7 0-10milesRun8 0.50.60.7010 miles Run 80-50 miles Run 1 0-50 miles Run 6 0-50 miles Run 7 DF0.30.40-50 miles Run 8 CCD00.1 0.215Individual Latent Cancer Fatality Risk per Event 01.0E-051.0E-041.0E-03 MACCS2 and Weather Uncertainties for LCF Risk 2ACRSComment:
MACCS2 and Weather Uncertainties for LCF Risk 2 (cont.)
ACRS Comment: *Select a MELCOR realization that produced a small, but non-zero, contribution to the conditional LCF fatality consequences in the current SOARCA uncertainty
* A set of 11 results have then been used as metrics to select three representative source terms:
  - Latent Cancer Fatality (LCF) risk at 5 different locations (10 (10, 20 20, 30, 40 and 50 miles)
  - Fraction of inventory released for 5 radionuclides (Cs, I, Ba, Ce, Te)
  - Release time
* Goal is to choose three source terms whose metrics ranks come closest to 1/6, 1/2, and 5/6 among the population 19


results. 
Results: Cobweb Graph for Selected Source Terms 1.0 0.8 0.6 CDF F  0.4 02 0.2 0.0 50      20      10     30     40     Cs   Iodine  Ba   Ce   Te   (hr) miles  miles  miles  miles  miles Metric 20
*Forthatrealizationsamplefromthe350MACCS2input For that realization
 
, sample from the 350 MACCS2 input parameters, and for each epistemic sample generate 984 weather cases to derive an uncertainty distribution for the ditilLCFftlitthdit conditional LCF fatality consequences a t each distance.  *Demonstrate convergence of the combined MACCS2-weatheruncertaintyanalysisresults.
MACCS2 and Weather Uncertainties for LCF Risk 2 (cont.)
16weather uncertainty analysis results.
Approach (cont (cont.):):
MACCS2 and Weather UncertaintiesforLCFRisk2 Uncertainties for LCF Risk 2 (cont.)Approach: *Three re presentative source terms were chosen p*First an initial MACCS2 run (Run 2) used all 865 source terms while all MACCS2 parameters were set to their SOARCApointestimatevalues SOARCA point estimate values.-To assess the influence of the source term when MACCS2 parameters are fixed 17 Run 2 Conditional Mean, Individual LCF Risk (per Event) for 865 Source Terms and Fixed CCDF 0.910-10miles0.70.80-10 miles0-20 miles 0-30 miles DF040.5 0.60-40 miles 0-50 miles CCD0.2 0.30.400.110E0510E0410E0310E0218Individual LCF Risk per Event 1.0E-051.0E-041.0E-031.0E-02 MACCS2 and Weather UncertaintiesforLCFRisk2 Uncertainties for LCF Risk 2 (cont.)*A set of 11 results have then been used as metrics to select three representative source terms:LatentCancerFatality(LCF)riskat5differentlocations(1020
* With respect to conditional LCF risk:
-Latent Cancer Fatality (LCF) risk at 5 different locations (10, 20, 30, 40 and 50 miles)-Fraction of inventory released for 5 radionuclides (Cs, I, Ba, Ce, Te)Te)-Release time*Goal is to choose three source terms whose metrics' ranks come closest to 1/6, 1/2, and 5/6 among the population 19 Results: Cobweb Graph for Selected Source Terms 1.00.8F0.6CDF020.40.00.220Metric50 miles20 miles10 miles30 miles40 milesCsIodineBaCeTe(hr)
  - MELCOR Replicate 3, Realization 187 identified as the representative low source term
MACCS2 and Weather UncertaintiesforLCFRisk2 Uncertainties for LCF Risk 2 (cont.)Approach(cont):
  - MELCOR Replicate 1, Realization 75 identified as the representative medium source term
Approach (cont.): *With respect to conditional LCF risk: -MELCOR Replicate 3, Realization 187 identified as the representative low source term-MELCOR Replicate 1, Realization 75 identified as the representative medium source term-MELCOR Replicate 1, Realization 290 identified as the representative high source term*For each of these source terms
  - MELCOR Replicate 1, Realization 290 identified as the representative high source term
, three Monte Carlo runs  
* For each of these source terms,, three Monte Carlo runs of sample size 1000 were completed (Runs 9-11,12-14, 15-17 respectively) using three different LHS random seeds for the 350 MACCS2 input parameters
,of sample size 1000 were completed (Runs 9-11,12-14, 15-17 respectively) using three different LHS random seedsforthe350MACCS2inputparameters 21seeds for the 350 MACCS2 input parameters*The same 984 weather trials were used.
* The same 984 weather trials were used.
Runs 9-11 (Low Source Term) Conditional,Mean,IndividualLCFRun#010020030040050Conditional, Mean, Individual LCF Risk (per event) Statistics Statistic Run #0-10 miles0-20 miles0-30 miles0-40 miles0-50 milesRun 91.1E-041.2E-048.3E-055.4E-054.4E-05 MeanRun1011E-0412E-0483E-0554E-0544E-05MeanRun 101.1E-041.2E-048.3E-055.4E-054.4E-05Run 111.1E-041.2E-048.3E-055.4E-054.4E-05 Run 98.8E-051.0E-047.2E-054.7E-053.9E-05 MedianRun1086E-0510E-0474E-0548E-0539E-05MedianRun 108.6E-051.0E-047.4E-054.8E-053.9E-05Run 118.8E-051.0E-047.2E-054.7E-053.9E-05 5thRun 92.3E-053.8E-052.7E-051.7E-051.4E-05 percentile Run1022E0538E0526E0517E0514E05percentile Run 102.2E-053.8E-052.6E-051.7E-051.4E-05Run 112.3E-054.0E-052.7E-051.8E-051.4E-05 95thRun 92.5E-042.4E-041.7E-041.1E-048.9E-05 percentileRun1026E0424E0417E0412E0495E0522percentile Run 102.6E-042.4E-041.7E-041.2E-049.5E-05Run 112.7E-042.4E-041.7E-041.1E-049.4E-05 Runs 9-11 and Run 1 Epistemic Uncertainty Conditional, Mean, Individual LCF Risk (per Event) CCDFs 0910-10 miles Run 1 010milesRun9 070.80.90-10 miles Run 90-10 miles Run 100-10 miles Run 11 050ilR1DF0.50.60.70-50 miles Run 10-50 miles Run 9 0-50 miles Run 10 CCD0.30.40-50 miles Run 11 0.1 0.223Individual LCF Risk per Event 01.0E-061.0E-051.0E-041.0E-03 Runs 12-14 (medium) and Run 1 Epistemic Uncertainty Conditional, Mean, Individual LCFRik(Et)CCDF LCF Risk (per Event) CCDFs0910-10 miles Run 1 0-10milesRun12 0.70.80.90-10 miles Run 120-10 miles Run 13 0-10 miles Run 14 DF0.5 0.60-50 miles Run 1 0-50 miles Run 12 0-50 miles Run 13 CCD020.30.40-50 miles Run 14 00.10.224Individual LCF Risk per Event 01.0E-061.0E-051.0E-041.0E-03 Runs 15-17 (high) and Run 1 Epistemic Uncertainty Conditional, Mean, Individual LCF Risk (per Event) CCDFs 0.910-10 miles Run 1 0-10milesRun15 0.70.8010 miles Run 150-10 miles Run 16 0-10 miles Run 17 0-50 miles Run 1 050milesRun15 DF0.5 0.60-50 miles Run 150-50 miles Run 16 0-50 miles Run 17 CCD020.30.400.10.225Individual LCF Risk per Event 01.0E-061.0E-051.0E-041.0E-03 Average difference between the three separate LHS runs over all pAleatory Weather Distributions (1 stto 99thpercentile)Source Term Conditional LCF RiskConditional LCF Risk0-10 miles0-50 miles Highest Prompt Fatality Risk-Runs3-50.8%0.8%Risk Runs 35Highest LCF Risk -
21
 
Runs 9-11 (Low Source Term)
Conditional, Mean, Individual LCF Risk (per event) Statistics Run #   0-10 0 10    0-20 0 20    0-30 0 30    0-40 0 40    0-50 0 50 Statistic miles  miles    miles    miles  miles Run 9  1.1E-04 1.2E-04  8.3E-05  5.4E-05 4.4E-05 Mean    Run 10 1 1E-04 1.1E-04 1 2E-04 1.2E-04  8 3E-05 8.3E-05  5 4E-05 5.4E-05 4 4E-05 4.4E-05 Run 11 1.1E-04 1.2E-04  8.3E-05  5.4E-05 4.4E-05 Run 9  8.8E-05 1.0E-04  7.2E-05  4.7E-05 3.9E-05 Median    Run 10 8 6E-05 8.6E-05 1 0E-04 1.0E-04  7 4E-05 7.4E-05  4 8E-05 4.8E-05 3 9E-05 3.9E-05 Run 11 8.8E-05 1.0E-04  7.2E-05  4.7E-05 3.9E-05 5th    Run 9  2.3E-05 3.8E-05  2.7E-05  1.7E-05 1.4E-05 percentile Run 10 2 2E 05 2.2E-05 3 8E 05 3.8E-05  2 6E 05 2.6E-05  1 7E 05 1.7E-05 1 4E 05 1.4E-05 Run 11 2.3E-05 4.0E-05  2.7E-05  1.8E-05 1.4E-05 95th    Run 9  2.5E-04 2.4E-04  1.7E-04  1.1E-04 8.9E-05 percentile Run 10 2 6E 04 2.6E-04 2 4E 04 2.4E-04  1 7E 04 1.7E-04  1 2E 04 1.2E-04 9 5E 05 9.5E-05 Run 11 2.7E-04 2.4E-04  1.7E-04  1.1E-04 9.4E-05 22
 
Runs 9-11 and Run 1 Epistemic Uncertainty Conditional, Mean, Individual LCF Risk (per Event) CCDFs 1                                      0-10 miles Run 1 09 0.9                                    0 10 miles Run 9 0-10 0-10 miles Run 10 0.8 0-10 miles Run 11 07 0.7                                    0 50 miles 0-50 il R Run 1 0.6                                    0-50 miles Run 9 0.5                                    0-50 miles Run 10 CCD DF                                          0-50 miles Run 11 0.4 0.3 0.2 0.1 0
1.0E-06  1.0E-05          1.0E-04          1.0E-03 Individual LCF Risk per Event                23
 
Runs 12-14 (medium) and Run 1 Epistemic Uncertainty Conditional, Mean, Individual LCF Risk Ri k (per
(    E Event) t) CCDFs CCDF 1                                          0-10 miles Run 1 09 0.9                                          0-10 miles Run 12 0.8                                          0-10 miles Run 13 0-10 miles Run 14 0.7 0-50 miles Run 1 0.6 0-50 miles Run 12 0.5 0-50 miles Run 13 CCD DF  0.4                                          0-50 miles Run 14 0.3 02 0.2 0.1 0
1.0E-06  1.0E-05          1.0E-04              1.0E-03 Individual LCF Risk per Event                      24
 
Runs 15-17 (high) and Run 1 Epistemic Uncertainty Conditional, Mean, Individual LCF Risk (per Event) CCDFs 1
0-10 miles Run 1 0.9                                      0-10 0 10 miles Run 15 0-10 miles Run 16 0.8                                      0-10 miles Run 17 0.7                                      0-50 miles Run 1 0 50 miles Run 15 0-50 0.6                                      0-50 miles Run 16 0-50 miles Run 17 0.5 CCD DF  0.4 0.3 02 0.2 0.1 0
1.0E-06  1.0E-05          1.0E-04            1.0E-03 Individual LCF Risk per Event                  25
 
Average difference between the three separate p      LHS runs over all Aleatory Weather Distributions (1st to 99th percentile)
Conditional  Conditional Source Term             LCF Risk    LCF Risk 0-10 miles  0-50 miles Highest Prompt Fatality 0.8%        0.8%
Risk - Runs 3-5 35 Highest LCF Risk -
0.8%        0.9%
Runs 6-8 L
Low  - Runs R    9 9-11 11                  0.9%
0 9%        0.8%
0 8%
Medium - Runs 12-14              0.8%         0.9%
High - Runs 15-17 15 17                1 0%
1.0%        0 6%
0.6%
Overall Average                  0.9%        0.8%
26


Runs 6-80.8%0.9%LR91109%08%Low -Runs 9-110.9%0.8%Medium -Runs 12-140.8%0.9%High-Runs15-1710%06%26High Runs 15171.0%0.6%Overall Average0.9%0.8%
MACCS2 Stability Analysis Using Bootstrap Approach Approach:
MACCS2 Stability Analysis Using Bootstrap Approach Approach:
Approach:*MACCS2 code modified to allow simple random sampling *The 'high' source term (i.e., Replicate 1 Realization 290
* MACCS2 code modified to allow simple random sampling
) g(p)and the SOARCA UA MACCS2 Analysis (Run 1) were selected to compare between Simple Random Sampling (SRSorMC)andLatinHypercubeSampling(LHS)in (SRS or MC) and Latin Hypercube Sampling (LHS) in order to validate the use of LHS*Bootstrapping performed (similar to approach with MELCOR results) to estimate confidence boundsConclusion:Resultsoftheuncertaintyanalysisarewell 27*Conclusion:
* The high g source term ((i.e., Replicate p      1 Realization 290))
Results of the uncertainty analysis are well converged and LHS use is valid Run 1 (CAP17) Conditional, Mean, Individual LCF Risk (per Event) CCDF with LHS and MC Sampling 0910.70.80.9DF0.5 0.60-10 miles CAP17-LHS CCD0.30.40-10 miles CAP17-MC 0-50 miles CAP17-LHS 00.1 0.20-50 miles CAP17-MC 28Individual LCF Risk per Event 01.0E-061.0E-051.0E-041.0E-03 Run 1 (CAP17) Conditional, Mean, Individual Prompt Fatality Risk (per Event) CCDF with LHS and MC Sampling 101DF0.1CCD0.010-1.3 miles CAP17-LHS 0-1.3 miles CAP17-MC 02milesCAP17 LHS00010-2 miles CAP17-LHS0-2 miles CAP17-MC 0-3.5 miles CAP17-LHS 0-3.5 miles CAP17-MC 29Individual Prompt Fatality Risk per Event 0.0011.0E-121.0E-111.0E-101.0E-091.0E-081.0E-071.0E-061.0E-05 10-mile Conditional, Mean, Individual LCF Risk (per Event) CDF for Run 15 (CAP37) and 95% ConfidenceIntervalUpperandLowerBounds Confidence Interval Upper and Lower Bounds for Runs 16 & 17 (CAP38 & 39) with SRS 30 50-mile Conditional, Mean, Individual LCF Risk (per Event) CDF for Run 15 (CAP37) and 95% ConfidenceIntervalUpperandLowerBounds Confidence Interval Upper and Lower Bounds for Runs 16 & 17 (CAP38 & 39) with SRS 31 MELCOR Parameters of Interest SRVLAM -SRV stochastic failure to reclose33 CHEMFORM -Iodine and cesium fraction ParameterDistributionCHEMFORM:FivealternativecombinationsofRN classes2,4,16,and17(CsOH,I 2,CsI,andCs 2MoO4)Discrete distributionCombination #1 = 0.125Combination#2
and the SOARCA UA MACCS2 Analysis (Run 1) were selected to compare between Simple Random Sampling (SRS or MC) and Latin Hypercube Sampling (LHS) in order to validate the use of LHS
=0125Notethefractioncesiumbelowrepresentsthe distributionof'residual'cesiumwhichisthemassof cesiumremainingafterfirstreactingwiththeamountof iodineassumedtoformCsICombination
* Bootstrapping performed (similar to approach with MELCOR results) to estimate confidence bounds
#2  0.125Combination #3 = 0.125 Combination #4 = 0.125Combination#5=0500 iodineassumedtoformCsI.Combination
 
#5 = 0.500Five AlternativesSpecies (MELCOR RN Class)CsOH (2)I 2 (4)CsI (16)Cs 2MO4 (17)fractioniodine--003097--Combination#1 fractioniodine0.030.97fractioncesium1----0Combination#2fractioniodine--0.0020.998fractioncesium0.5----0.5 fractioniodine--000298099702--Combination#3 fractioniodine0.002980.99702fractioncesium0----1Combination#4fractioniodine--0.07570.9243--fractioncesium0.5----0.5 fractioniodine--0027709723--34Combination#5 fractioniodine--0.02770.9723--fractioncesium0----1SOARCAestimateFractioniodine--0.01.0--Fractioncesium0.0----1.0 FL904A -Drywell liner failure flow area 35 BATTDUR -Battery Duration 36 SRVOAFRAC -SRV open area fraction 37 SLCRFRAC -Main steam line creep rupture area fraction 38 Radial debris relocation time constants -RDSTC (solid) 39 Radial debris relocation time constants -RDMTC (liquid)40 RRIDRFRAC, RODRFRAC -Railroad door open fraction 41 H2IGNC -Hydrogen ignition criteria 42 RHONOM -Particle density 43 FFC -Fuel failure criterion 44 FFC -Fuel failure criterion (continued) 45 SC1141(2) -Molten clad drainage rate 46 Other MELCOR Items of Interest*Surrogateparameters
== Conclusion:==
*Surrogate parametersLhdttifil
Results of the uncertainty analysis are well converged and LHS use is valid 27
*Lower head penetration failuresDlllifildl
 
*Drywell liner failure model*Operator actions 47 MACCS2 Parameters of Interest DOSNRM, DOSHOT -Normal and Hotspot Relocation Doses 49 TIMNRM, TIMHOT -Normal and Hotspot Relocation Times50 ESPEED -Evacuation speed 51 GSHFAC -Groundshine Shielding Factor52 Next Stepsp*ANSPSAConferencepresentationand papers*ANS PSA Conference presentation and papers and CSARP presentation
Run 1 (CAP17) Conditional, Mean, Individual LCF Risk (per Event) CCDF with LHS and MC Sampling 1
-September2013 September 2013 *Send final NUREG/CR-7155 report for publication -Fall 2013 53 Questions and Comments Note that all results in these presentation slides are conditional (per event) on the potentialoccurrenceofalong
09 0.9 0.8 0.7 0.6 0.5       0-10 miles CAP17-LHS CCD DF  0.4        0-10 miles CAP17-MC 0.3 0-50 miles CAP17-LHS 0.2 0-50 miles CAP17-MC 0.1 0
-termstation potential occurrence of a longterm station blackout (LTSBO) scenario, and modeling the SOARCA unmitigated LTSBO. ThLTSBOifititd The LTSBO scenario frequency is estimated in SOARCA to be ~3x10
1.0E-06          1.0E-05            1.0E-04        1.0E-03 Individual LCF Risk per Event            28
-6per reactor year.}}
 
Run 1 (CAP17) Conditional, Mean, Individual Prompt Fatality Risk (per Event) CCDF with LHS and MC Sampling 1
01 0.1 CCD DF 0.01          0-1.3 miles CAP17-LHS 0-1.3 miles CAP17-MC 0 2 miles CAP17 0-2       CAP17-LHS LHS 0-2 miles CAP17-MC 0-3.5 miles CAP17-LHS 0.001 0 001          0-3.5 miles CAP17-MC 1.0E-12 1.0E-11 1.0E-10 1.0E-09 1.0E-08 1.0E-07 1.0E-06 1.0E-05 Individual Prompt Fatality Risk per Event    29
 
10-mile Conditional, Mean, Individual LCF Risk (per Event) CDF for Run 15 (CAP37) and 95%
Confidence Interval Upper and Lower Bounds for Runs 16 & 17 (CAP38 & 39) with SRS 30
 
50-mile Conditional, Mean, Individual LCF Risk (per Event) CDF for Run 15 (CAP37) and 95%
Confidence Interval Upper and Lower Bounds for Runs 16 & 17 (CAP38 & 39) with SRS 31
 
MELCOR Parameters of Interest
 
SRVLAM - SRV stochastic failure to reclose 33
 
CHEMFORM - Iodine and cesium fraction Parameter                                        Distribution CHEMFORM: Five alternative combinations of RN                        Discrete distribution classes 2, 4, 16, and 17 (CsOH, I2, CsI, and Cs2MoO4)               Combination #1 = 0.125 Combination #2 = 0.125 0 125 Note the fraction cesium below represents the                        Combination #3 = 0.125 distribution of 'residual' cesium which is the mass of              Combination #4 = 0.125 cesium remaining after first reacting with the amount of iodine assumed to form CsI.
CsI                                        Combination #5 = 0.500 0 500 Five Alternatives                          Species (MELCOR RN Class)
CsOH (2)       I2 (4)       CsI (16)     Cs2MO4 (17) fraction iodine      --           0.03 0  03          0.97 0  97            --
Combination #1 fraction cesium      1            --             --               0 fraction iodine      --           0.002          0.998 Combination #2 fraction cesium      0.5         --             --               0.5 fraction iodine      --           0 00298 0.00298        0 99702 0.99702          --
Combination #3 fraction cesium      0            --             --               1 fraction iodine      --           0.0757          0.9243           --
Combination #4 fraction cesium      0.5         --             --               0.5 fraction iodine      --           0 0277 0.0277          0 9723 0.9723           --
Combination #5 fraction cesium      0            --             --               1 Fraction iodine      --           0.0            1.0             --
SOARCA estimate Fraction cesium      0.0         --             --               1.0         34
 
FL904A - Drywell liner failure flow area 35
 
BATTDUR - Battery Duration 36
 
SRVOAFRAC - SRV open area fraction 37
 
SLCRFRAC - Main steam line creep rupture area fraction 38
 
Radial debris relocation time constants - RDSTC (solid) 39
 
Radial debris relocation time constants - RDMTC (liquid) 40
 
RRIDRFRAC, RODRFRAC - Railroad door open fraction 41
 
H2IGNC - Hydrogen ignition criteria 42
 
RHONOM - Particle density 43
 
FFC - Fuel failure criterion 44
 
FFC - Fuel failure criterion (continued) 45
 
SC1141(2) - Molten clad drainage rate 46
 
Other MELCOR Items of Interest
* Surrogate parameters
* Lower L      h head   d penetration t ti ffailures il
* Drywell D    ll liliner ffailure il    model d l
* Operator actions 47
 
MACCS2 Parameters of Interest
 
DOSNRM, DOSHOT - Normal and Hotspot Relocation Doses 49
 
TIMNRM, TIMHOT - Normal and Hotspot Relocation Times 50
 
ESPEED - Evacuation speed 51
 
GSHFAC - Groundshine Shielding Factor 52
 
Next Steps p
* ANS PSA Conference presentation and papers and CSARP presentation
  - September 2013
* Send final NUREG/CR-7155 report for publication - Fall 2013 53
 
Questions and Comments Note that all results in these presentation slides are conditional (per event) on the potential occurrence of a long-term long term station blackout (LTSBO) scenario, and modeling the SOARCA unmitigated LTSBO.
Th LTSBO scenario The                  i frequency f          is i estimated ti t d in SOARCA to be ~3x10-6 per reactor year.}}

Latest revision as of 01:43, 6 February 2020

Soarca Peach Bottom Uncertainty Analysis (UA) Acrc Briefing - Sept 2013
ML13255A376
Person / Time
Site: Peach Bottom  Constellation icon.png
Issue date: 09/16/2013
From: Tina Ghosh
NRC/RES/DSA
To:
Ghosh T
Shared Package
ML13255A375 List:
References
Download: ML13255A376 (55)
v • d • e


Text

SOARCA Peach Bottom Uncertainty Analysis (UA)

ACRS Briefing Tina Ghosh, PhD RES/DSA/AAB September 16, 2013

Agenda

  • ACRS comments t on MACCS2 weatherth uncertainty integration and convergence of results and staff responses results,
  • MELCOR parameters of interest
  • MACCS2 parameters of interest 2

MELCOR - MACCS2 -

Weather Uncertainty Integration ACRS Comment:

  • For the combined MELCOR-MACCS2 results, the report currently presents only results averaged over the weather trials.
  • The report should also present results that include and di l the display th full f ll weather th aleatory l t uncertainty t i t 3

Conditional mean, individual latent cancer fatality y ((LCF)) risk (p (per event) for combined results (865) with LNT model 0 10 0-10 0 20 0-20 0 30 0-30 0 40 0-40 0 50 0-50 miles miles miles miles miles 5th 3.1x10 3 1 10-55 4 4.9x10 9 10-55 3 3.4x10 4 10-55 2 2.2x10 2 10-55 1 1.9x10 9 10-55 percentile Median 1.3x10-4 1.9x10-4 1.3x10-4 8.7x10-5 7.1x10-5 Mean 1.7x10-4 2.8x10-4 2.0x10-4 1.3x10-4 1.0x10-4 95th 4.2x10-4 7.7x10-4 5.3x10-4 3.4x10-4 2.7x10-4 percentile SOARCA UA 9.0x10-5 8.3x10-5 5.8x10-5 3.7x10-5 3.0x10-5 Base Case 4

Conditional Individual LCF Risk (per Event) CCDFs for Combined Aleatory and d Epistemic E i t i Uncertainty U t i t andd Epistemic Uncertainty with Aleatory Means 1 0-10 miles Aleatory Mean 0.9 0-20 miles Aleatory Mean 0-50 miles Aleatory Mean 0.8 0-10 miles Epistemic & Aleatory 0 20 miles 0-20 il E Epistemic i t i & Al Aleatory t

0.7 0-50 miles Epistemic & Aleatory 0.6 05 0.5 CCD DF 0.4 0.3 0.2 0.1 0

1.0E-06 1.0E-05 1.0E-04 1.0E-03 1.0E-02 Individual LCF Risk 5

MACCS2 and Weather Uncertainties for Prompt Fatality Risk ACRS Comment:

  • Select the MELCOR realization that produced the largest conditional prompt fatality consequences in the current SOARCA uncertainty results.
  • For that realization, sample from the 350 MACCS2 input parameters and for each epistemic sample generate 984 parameters, weather cases to derive an uncertainty distribution for the conditional prompt fatality consequences at each di t distance.
  • Demonstrate convergence of the combined MACCS2-weather uncertainty analysis results.

6

MACCS2 and Weather Uncertainties for Prompt Fatality Risk (cont.)

Approach:

  • MELCOR Replicate 2, Realization 291 identified as the source term that produced the largest conditional prompt fatality risk consequence
  • For that source term, three Monte Carlo runs of sample size 1000 were completed (Runs 3 3, 4 4, 5) using three different LHS random seeds for the 350 MACCS2 input parameters
  • The same 984 weather trials were used 7

Conditional, mean, individual prompt-fatality p p y risk (p (per event))

statistics for the MACCS2 Uncertainty Analysis for specified circular areas (Run 1) 0-1.3 0-2.5 0-3.5 0-7 0-10 miles miles miles miles miles Mean 4.5x10-7 8.9x10-8 3.5x10-8 8.3x10-9 4.8x10-9 Median 00 0.0 00 0.0 00 0.0 00 0.0 00 0.0 75th percent p 0.0 0.0 0.0 0.0 0.0

-ile 95th 1.9x10 percent 1 9x10-6 3 3.5x10 5x10-8 00 0.0 00 0.0 00 0.0

-ile 8

Run 3-5 conditional, mean, individual p prompt-fatality p y risk (p (per event) statistics for specified circular areas 0-1.3 0-2.5 0-3.5 0-10 0-7 miles miles miles miles miles Run 3 3.3E-06 1.0E-06 3.4E-07 4.7E-08 9.5E-09 M

Mean R Run 4 3 3E 06 3.3E-06 9 4E 07 9.4E-07 3 0E 07 3.0E-07 4 2E 08 4.2E-08 8 9E 09 8.9E-09 Run 5 3.2E-06 9.8E-07 3.0E-07 4.7E-08 1.3E-08 Run 3 4.9E-07 1.2E-07 0.0 0.0 0.0 Median Run 4 3 3E 06 3.3E-06 9 4E 07 9.4E-07 00 0.0 00 0.0 00 0.0 Run 5 3.2E-06 9.8E-07 0.0 0.0 0.0 75th Run 3 4.0E-06 1.0E-06 2.0E-07 3.8E-09 0.0 percent Run 4 3 7E 06 3.7E-06 8 8E 07 8.8E-07 2 2E 07 2.2E-07 1 1E 08 1.1E-08 00 0.0

-ile Run 5 3.9E-06 9.6E-07 1.9E-07 8.2E-09 0.0 95th Run 3 1.4E-05 4.1E-06 1.5E-06 2.1E-07 1.2E-08 percent Run 4 1 6E 05 1.6E-05 4 7E 06 4.7E-06 1 8E 06 1.8E-06 2 3E 07 2.3E-07 00 0.0

-ile Run 5 1.4E-05 4.4E-06 1.6E-06 2.0E-07 0.0 9

Runs 3-5 and Run 1 Conditional, Mean Individual Prompt Fatality Risk (per Event) Epistemic Uncertainty CCDF, at 1.3 Miles 1

0.1 0-1.3 miles Run 1 CCD DF 0-1.3 miles Run 3 0.01 0-1.3 miles Run 4 0-1.3 miles Run 5 0.001 1.0E-11 1.0E-10 1.0E-09 1.0E-08 1.0E-07 1.0E-06 1.0E-05 1.0E-04 Individual Prompt Fatality Risk per Event 10

Runs 3-5 and Run 1 Conditional, Mean Individual Prompt Fatality Risk (per Event) Epistemic Uncertainty CCDF, at 3.5 Miles 1

0.1 0-3.5 miles Run 1 0-3 0 3.5 5 miles Run 3 CCD DF 0-3.5 miles Run 4 0.01 0-3.5 miles Run 5 0.001 0 001 1.0E-12 1.0E-11 1.0E-10 1.0E-09 1.0E-08 1.0E-07 1.0E-06 1.0E-05 Individual Prompt Fatality Risk per Event 11

MACCS2 and Weather Uncertainties for LCF Risk 1 ACRS Comment:

  • Select the MELCOR realization that produced the largest conditional LCF fatality consequences in the current SOARCA uncertainty results.
  • For that realization, sample from the 350 MACCS2 input parameters and for each epistemic sample generate 984 parameters, weather cases to derive an uncertainty distribution for the conditional LCF fatality consequences at each distance.
  • Demonstrate convergence of the combined MACCS2-weather uncertainty analysis results.

12

MACCS2 and Weather Uncertainties for LCF Risk 1 (cont.)

Approach:

  • MELCOR Replicate 3, Realization 46 identified as the source term that produced the largest conditional LCF risk consequence
  • For that source term, three Monte Carlo runs of sample size 1000 were completed (Runs 6 6, 7 7, 8) using three different LHS random seeds for the 350 MACCS2 input parameters
  • The same 984 weather trials were used 13

Run 6-8 Combined Aleatory and Epistemic Uncertainty Conditional Individual LCF Risk (per Event) CCDF 1

0.9 0-10 miles Run 6 0.8 0-10 miles Run 7 07 0.7 0-10 miles Run 8 0.6 0-50 miles Run 6 0.5 0-50 miles Run 7 CC CDF 0.4 0-50 miles Run 8 0.3 02 0.2 0.1 0

1.E-05 1.E-04 1.E-03 1.E-02 Individual Latent Cancer Fatality Risk per Event 14

Runs 6-8 and Run 1 Epistemic Uncertainty with Aleatory Mean, Conditional Individual LCF Risk (per Event) CCDFs 1

0-10 0 10 miles Run 1 0.9 0-10 miles Run 6 0.8 0-10 miles Run 7 07 0.7 0-10 0 10 miles Run 8 0-50 miles Run 1 0.6 0-50 miles Run 6 0.5 0-50 miles Run 7 CCD DF 0.4 0-50 miles Run 8 0.3 0.2 0.1 0

1.0E-05 1.0E-04 1.0E-03 Individual Latent Cancer Fatality Risk per Event 15

MACCS2 and Weather Uncertainties for LCF Risk 2 ACRS Comment:

  • Select a MELCOR realization that produced a small, but non-zero, contribution to the conditional LCF fatality consequences in the current SOARCA uncertainty results.

realization sample from the 350 MACCS2 input

  • For that realization, parameters, and for each epistemic sample generate 984 weather cases to derive an uncertainty distribution for the conditional diti l LCF ffatality t lit consequences att each h di distance.

t

  • Demonstrate convergence of the combined MACCS2-weather uncertainty analysis results.

16

MACCS2 and Weather Uncertainties for LCF Risk 2 (cont.)

Approach:

  • Three representative p source terms were chosen
  • First an initial MACCS2 run (Run 2) used all 865 source terms while all MACCS2 parameters were set to their SOARCA point estimate values values.

- To assess the influence of the source term when MACCS2 parameters are fixed 17

Run 2 Conditional Mean, Individual LCF Risk (per Event) for 865 Source Terms and Fixed CCDF 1

0.9 0-10 miles 0.8 0-20 miles 0.7 0-30 miles 0.6 0-40 miles 0.5 0-50 miles 04 0.4 CCD DF 0.3 0.2 0.1 0

1 0E 05 1.0E-05 1 0E 04 1.0E-04 1 0E 03 1.0E-03 1 0E 02 1.0E-02 Individual LCF Risk per Event 18

MACCS2 and Weather Uncertainties for LCF Risk 2 (cont.)

  • A set of 11 results have then been used as metrics to select three representative source terms:

- Latent Cancer Fatality (LCF) risk at 5 different locations (10 (10, 20 20, 30, 40 and 50 miles)

- Fraction of inventory released for 5 radionuclides (Cs, I, Ba, Ce, Te)

- Release time

  • Goal is to choose three source terms whose metrics ranks come closest to 1/6, 1/2, and 5/6 among the population 19

Results: Cobweb Graph for Selected Source Terms 1.0 0.8 0.6 CDF F 0.4 02 0.2 0.0 50 20 10 30 40 Cs Iodine Ba Ce Te (hr) miles miles miles miles miles Metric 20

MACCS2 and Weather Uncertainties for LCF Risk 2 (cont.)

Approach (cont (cont.):):

  • With respect to conditional LCF risk:

- MELCOR Replicate 3, Realization 187 identified as the representative low source term

- MELCOR Replicate 1, Realization 75 identified as the representative medium source term

- MELCOR Replicate 1, Realization 290 identified as the representative high source term

  • For each of these source terms,, three Monte Carlo runs of sample size 1000 were completed (Runs 9-11,12-14, 15-17 respectively) using three different LHS random seeds for the 350 MACCS2 input parameters
  • The same 984 weather trials were used.

21

Runs 9-11 (Low Source Term)

Conditional, Mean, Individual LCF Risk (per event) Statistics Run # 0-10 0 10 0-20 0 20 0-30 0 30 0-40 0 40 0-50 0 50 Statistic miles miles miles miles miles Run 9 1.1E-04 1.2E-04 8.3E-05 5.4E-05 4.4E-05 Mean Run 10 1 1E-04 1.1E-04 1 2E-04 1.2E-04 8 3E-05 8.3E-05 5 4E-05 5.4E-05 4 4E-05 4.4E-05 Run 11 1.1E-04 1.2E-04 8.3E-05 5.4E-05 4.4E-05 Run 9 8.8E-05 1.0E-04 7.2E-05 4.7E-05 3.9E-05 Median Run 10 8 6E-05 8.6E-05 1 0E-04 1.0E-04 7 4E-05 7.4E-05 4 8E-05 4.8E-05 3 9E-05 3.9E-05 Run 11 8.8E-05 1.0E-04 7.2E-05 4.7E-05 3.9E-05 5th Run 9 2.3E-05 3.8E-05 2.7E-05 1.7E-05 1.4E-05 percentile Run 10 2 2E 05 2.2E-05 3 8E 05 3.8E-05 2 6E 05 2.6E-05 1 7E 05 1.7E-05 1 4E 05 1.4E-05 Run 11 2.3E-05 4.0E-05 2.7E-05 1.8E-05 1.4E-05 95th Run 9 2.5E-04 2.4E-04 1.7E-04 1.1E-04 8.9E-05 percentile Run 10 2 6E 04 2.6E-04 2 4E 04 2.4E-04 1 7E 04 1.7E-04 1 2E 04 1.2E-04 9 5E 05 9.5E-05 Run 11 2.7E-04 2.4E-04 1.7E-04 1.1E-04 9.4E-05 22

Runs 9-11 and Run 1 Epistemic Uncertainty Conditional, Mean, Individual LCF Risk (per Event) CCDFs 1 0-10 miles Run 1 09 0.9 0 10 miles Run 9 0-10 0-10 miles Run 10 0.8 0-10 miles Run 11 07 0.7 0 50 miles 0-50 il R Run 1 0.6 0-50 miles Run 9 0.5 0-50 miles Run 10 CCD DF 0-50 miles Run 11 0.4 0.3 0.2 0.1 0

1.0E-06 1.0E-05 1.0E-04 1.0E-03 Individual LCF Risk per Event 23

Runs 12-14 (medium) and Run 1 Epistemic Uncertainty Conditional, Mean, Individual LCF Risk Ri k (per

( E Event) t) CCDFs CCDF 1 0-10 miles Run 1 09 0.9 0-10 miles Run 12 0.8 0-10 miles Run 13 0-10 miles Run 14 0.7 0-50 miles Run 1 0.6 0-50 miles Run 12 0.5 0-50 miles Run 13 CCD DF 0.4 0-50 miles Run 14 0.3 02 0.2 0.1 0

1.0E-06 1.0E-05 1.0E-04 1.0E-03 Individual LCF Risk per Event 24

Runs 15-17 (high) and Run 1 Epistemic Uncertainty Conditional, Mean, Individual LCF Risk (per Event) CCDFs 1

0-10 miles Run 1 0.9 0-10 0 10 miles Run 15 0-10 miles Run 16 0.8 0-10 miles Run 17 0.7 0-50 miles Run 1 0 50 miles Run 15 0-50 0.6 0-50 miles Run 16 0-50 miles Run 17 0.5 CCD DF 0.4 0.3 02 0.2 0.1 0

1.0E-06 1.0E-05 1.0E-04 1.0E-03 Individual LCF Risk per Event 25

Average difference between the three separate p LHS runs over all Aleatory Weather Distributions (1st to 99th percentile)

Conditional Conditional Source Term LCF Risk LCF Risk 0-10 miles 0-50 miles Highest Prompt Fatality 0.8% 0.8%

Risk - Runs 3-5 35 Highest LCF Risk -

0.8% 0.9%

Runs 6-8 L

Low - Runs R 9 9-11 11 0.9%

0 9% 0.8%

0 8%

Medium - Runs 12-14 0.8% 0.9%

High - Runs 15-17 15 17 1 0%

1.0% 0 6%

0.6%

Overall Average 0.9% 0.8%

26

MACCS2 Stability Analysis Using Bootstrap Approach Approach:

  • MACCS2 code modified to allow simple random sampling
  • The high g source term ((i.e., Replicate p 1 Realization 290))

and the SOARCA UA MACCS2 Analysis (Run 1) were selected to compare between Simple Random Sampling (SRS or MC) and Latin Hypercube Sampling (LHS) in order to validate the use of LHS

  • Bootstrapping performed (similar to approach with MELCOR results) to estimate confidence bounds

Conclusion:

Results of the uncertainty analysis are well converged and LHS use is valid 27

Run 1 (CAP17) Conditional, Mean, Individual LCF Risk (per Event) CCDF with LHS and MC Sampling 1

09 0.9 0.8 0.7 0.6 0.5 0-10 miles CAP17-LHS CCD DF 0.4 0-10 miles CAP17-MC 0.3 0-50 miles CAP17-LHS 0.2 0-50 miles CAP17-MC 0.1 0

1.0E-06 1.0E-05 1.0E-04 1.0E-03 Individual LCF Risk per Event 28

Run 1 (CAP17) Conditional, Mean, Individual Prompt Fatality Risk (per Event) CCDF with LHS and MC Sampling 1

01 0.1 CCD DF 0.01 0-1.3 miles CAP17-LHS 0-1.3 miles CAP17-MC 0 2 miles CAP17 0-2 CAP17-LHS LHS 0-2 miles CAP17-MC 0-3.5 miles CAP17-LHS 0.001 0 001 0-3.5 miles CAP17-MC 1.0E-12 1.0E-11 1.0E-10 1.0E-09 1.0E-08 1.0E-07 1.0E-06 1.0E-05 Individual Prompt Fatality Risk per Event 29

10-mile Conditional, Mean, Individual LCF Risk (per Event) CDF for Run 15 (CAP37) and 95%

Confidence Interval Upper and Lower Bounds for Runs 16 & 17 (CAP38 & 39) with SRS 30

50-mile Conditional, Mean, Individual LCF Risk (per Event) CDF for Run 15 (CAP37) and 95%

Confidence Interval Upper and Lower Bounds for Runs 16 & 17 (CAP38 & 39) with SRS 31

MELCOR Parameters of Interest

SRVLAM - SRV stochastic failure to reclose 33

CHEMFORM - Iodine and cesium fraction Parameter Distribution CHEMFORM: Five alternative combinations of RN Discrete distribution classes 2, 4, 16, and 17 (CsOH, I2, CsI, and Cs2MoO4) Combination #1 = 0.125 Combination #2 = 0.125 0 125 Note the fraction cesium below represents the Combination #3 = 0.125 distribution of 'residual' cesium which is the mass of Combination #4 = 0.125 cesium remaining after first reacting with the amount of iodine assumed to form CsI.

CsI Combination #5 = 0.500 0 500 Five Alternatives Species (MELCOR RN Class)

CsOH (2) I2 (4) CsI (16) Cs2MO4 (17) fraction iodine -- 0.03 0 03 0.97 0 97 --

Combination #1 fraction cesium 1 -- -- 0 fraction iodine -- 0.002 0.998 Combination #2 fraction cesium 0.5 -- -- 0.5 fraction iodine -- 0 00298 0.00298 0 99702 0.99702 --

Combination #3 fraction cesium 0 -- -- 1 fraction iodine -- 0.0757 0.9243 --

Combination #4 fraction cesium 0.5 -- -- 0.5 fraction iodine -- 0 0277 0.0277 0 9723 0.9723 --

Combination #5 fraction cesium 0 -- -- 1 Fraction iodine -- 0.0 1.0 --

SOARCA estimate Fraction cesium 0.0 -- -- 1.0 34

FL904A - Drywell liner failure flow area 35

BATTDUR - Battery Duration 36

SRVOAFRAC - SRV open area fraction 37

SLCRFRAC - Main steam line creep rupture area fraction 38

Radial debris relocation time constants - RDSTC (solid) 39

Radial debris relocation time constants - RDMTC (liquid) 40

RRIDRFRAC, RODRFRAC - Railroad door open fraction 41

H2IGNC - Hydrogen ignition criteria 42

RHONOM - Particle density 43

FFC - Fuel failure criterion 44

FFC - Fuel failure criterion (continued) 45

SC1141(2) - Molten clad drainage rate 46

Other MELCOR Items of Interest

  • Surrogate parameters
  • Drywell D ll liliner ffailure il model d l
  • Operator actions 47

MACCS2 Parameters of Interest

DOSNRM, DOSHOT - Normal and Hotspot Relocation Doses 49

TIMNRM, TIMHOT - Normal and Hotspot Relocation Times 50

ESPEED - Evacuation speed 51

GSHFAC - Groundshine Shielding Factor 52

Next Steps p

  • ANS PSA Conference presentation and papers and CSARP presentation

- September 2013

Questions and Comments Note that all results in these presentation slides are conditional (per event) on the potential occurrence of a long-term long term station blackout (LTSBO) scenario, and modeling the SOARCA unmitigated LTSBO.

Th LTSBO scenario The i frequency f is i estimated ti t d in SOARCA to be ~3x10-6 per reactor year.

Retrieved from "https://kanterella.com/w/index.php?title=ML13255A376&oldid=2455396"